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arXiv · 2107.11373

Cardinality-constrained structured data-fitting problems

Zhenan Fan·Huang Fang·Michael P. Friedlander
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Abstract

A memory-efficient framework is described for the cardinality-constrained structured data-fitting problem. Dual-based atom-identification rules are proposed that reveal the structure of the optimal primal solution from near-optimal dual solutions. These rules allow for a simple and computationally cheap algorithm for translating any feasible dual solution to a primal solution that satisfies the cardinality constraint. Rigorous guarantees are provided for obtaining a near-optimal primal solution given any dual-based method that generates dual iterates converging to an optimal dual solution. Numerical experiments on real-world datasets support confirm the analysis and demonstrate the efficiency of the proposed approach.

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Zhenan Fan, Huang Fang, Michael P. Friedlander. 2021-07-23. Cardinality-constrained structured data-fitting problems. https://arxiv.org/abs/2107.11373

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